metadata

title: English Accent Detector
emoji: 🎤
colorFrom: blue
colorTo: green
sdk: docker
app_port: 8501
tags:
  - streamlit
  - audio
  - accent-detection
  - hiring
pinned: false
short_description: Detect and analyze English accents from videos
license: mit

🎤 English Accent Detection Tool

This app analyzes a speaker's English accent from video URLs or audio uploads, providing detailed insights for hiring evaluation purposes.

Features

Video URL Processing: Accept and analyze videos from Loom, YouTube, or direct MP4 links
Audio Upload Support: Directly upload audio files for analysis
English Accent Classification: Identify specific English accents (American, British, Australian, etc.)
Confidence Scoring: Get detailed confidence scores for English proficiency
Detailed Analysis: Receive expert-like explanations about accent characteristics
Visual Feedback: View audio waveforms and listen to the processed audio

Usage

Via Video URL:
- Enter a public video URL (Loom, YouTube, direct MP4, etc.)
- Click "Analyze Video"
- View the accent classification, confidence scores, and analysis
Via Audio Upload:
- Upload an audio file (WAV, MP3, M4A, OGG)
- Click "Analyze Audio"
- View the results

Troubleshooting YouTube Authentication Issues

If you encounter errors like Sign in to confirm you're not a bot when using YouTube videos:

Use a different video source (RECOMMENDED):
- Try using Loom, Vimeo, or direct MP4 links instead of YouTube
- These sources are more reliable and have fewer authentication requirements
Use alternative YouTube frontends:
- The app now automatically tries alternative YouTube frontends (Invidious, Piped)
- Make sure the "Try alternative YouTube source" checkbox is checked
- These frontend services can bypass some YouTube restrictions
Using cookies for YouTube authentication:
- The app supports uploading a cookies.txt file for YouTube authentication
- You can export cookies from your browser using browser extensions like "Get cookies.txt"
- Or use yt-dlp's built-in browser cookie extraction:
```
# Install yt-dlp if you don't have it
pip install yt-dlp

# Extract cookies from your browser (replace chrome with your browser)
yt-dlp --cookies-from-browser chrome -o cookies.txt https://youtube.com
```
- Upload the generated cookies file in the app interface
Update yt-dlp for better YouTube support:
- If running outside Docker, update yt-dlp to the latest version:
```
pip install -U yt-dlp
```
- YouTube frequently updates its anti-bot measures, and newer yt-dlp versions often have fixes

Technology Stack

Audio Processing: FFmpeg, Librosa
ML Models: SpeechBrain, Transformers
UI: Streamlit
Deployment: Docker

Requirements

Python 3.9+
FFmpeg
See requirements.txt for Python dependencies

Deployment

The app is containerized with Docker for easy deployment. Use the included Dockerfile to build and run:

# Build the Docker image
docker build -t accent-detector .

# Run the container with volume mounting for better file handling
docker run -p 8501:8501 --volume /tmp/accent-detector:/app/uploads accent-detector 

# For Windows users:
docker run -p 8501:8501 --volume C:\temp\accent-detector:/app/uploads accent-detector

Troubleshooting Upload Issues

If you encounter 403 Forbidden errors when uploading files:

Make sure your audio file is under 200MB
Try converting your audio to a WAV or MP3 format
For longer files, consider extracting just the speech segment
If uploading an MP4 video, ensure it's not encrypted or DRM-protected

Troubleshooting Hugging Face Cache Issues

If you encounter errors related to Hugging Face model loading or cache permissions:

Ensure proper volume mounts and permissions:

# Create local directories with proper permissions
mkdir -p /tmp/accent-detector /tmp/huggingface-cache
chmod 777 /tmp/accent-detector /tmp/huggingface-cache

# Run with multiple volume mounts for both uploads and cache
docker run -p 8501:8501 \
  --volume /tmp/accent-detector:/app/uploads \
  --volume /tmp/huggingface-cache:/app/.cache/huggingface \
  accent-detector

On Windows:

# Create directories
mkdir -Force C:\temp\accent-detector
mkdir -Force C:\temp\huggingface-cache

# Run with volume mounts
docker run -p 8501:8501 `
  --volume C:\temp\accent-detector:/app/uploads `
  --volume C:\temp\huggingface-cache:/app/.cache/huggingface `
  accent-detector

Reset the container and cache:

# Stop any running containers
docker stop $(docker ps -q --filter ancestor=accent-detector)

# Remove the container
docker rm $(docker ps -a -q --filter ancestor=accent-detector)

# Optionally clear the cache directories
rm -rf /tmp/accent-detector/* /tmp/huggingface-cache/*

# Rebuild and run
docker build --no-cache -t accent-detector .
docker run -p 8501:8501 --volume /tmp/accent-detector:/app/uploads --volume /tmp/huggingface-cache:/app/.cache/huggingface accent-detector

Spaces:

amirjamali
/

accent-detector

Running